A novel snow conditions‐compatible computational intelligence‐based PV power forecasting approach for microgrids in snow prone regions

Abstract

Abstract Energy management in a renewable energy‐based microgrid has a key role in improving energy utilisation and reducing the microgrid operation cost. The optimal energy management strategy can be significantly affected by the intermittency of renewable energies and also harsh weather conditions. In this study, a novel snow conditions‐compatible computational intelligence‐based short‐term photovoltaic (PV) power forecasting (PVPF) approach is proposed that is independent of exogenous weather forecasts. The proposed approach consists of a snow cover detection stage, a snow cover forecasting stage, and a PV power forecasting stage. This approach is then validated for a model predictive control (MPC)‐based energy management system (EMS) of a PV energy‐based grid‐connected microgrid located in a snow‐prone area. The PVPF method together with a computational intelligence‐based short‐term load demand forecasting model constitutes the forecasting block of the EMS. The forecasting block generates day‐ahead hourly forecasts based on the local measurements of the meteorological‐electrical parameters and sends them to the optimisation block where a two‐stage control method, corresponding to the tertiary and secondary control levels, is developed based on mixed‐integer linear and quadratic programming. The developed EMS is applied to a test microgrid simulated in MATLAB/Simulink and compared with a heuristic control method. The results show that the proposed approach can reduce the overall operation cost of the microgrid by 8% (24$), 15% (166$), and 13% (235$) on sunny, cloudy, and snowy days under study, respectively, compared to the heuristic controller.

Keywords

• artificial intelligence and data analytics
• battery storage plants
• microgrid, nanogrid, and peer‐to‐peer energy trading
• photovoltaic power systems